Feature selection on movement imagery discrimination and attention detection

Medical and Biological Engineering and Computing

Volumn 48, Issue 4, 2010, Pages 331-341

  Dias, N S ^a   Kamrunnahar, M ^b   Mendes, P M ^a   Schiff, S J ^b,c   Correia, J H ^a  

^a UNIVERSITY OF MINHO   (Portugal)

^b The Pennsylvania State University   (United States)

^c PENNSYLVANIA STATE UNIVERSITY   (United States)

Author keywords

Brain computer interface; EEG; Event related potentials; Feature selection; Movement imagery

Indexed keywords

ATTENTION DETECTION; CLASSIFICATION ACCURACY; CLASSIFICATION ERRORS; EVENT RELATED POTENTIALS; EVOKED RESPONSE; FEATURE SELECTION; FEATURE SELECTION ALGORITHM; FEATURE SETS; SELECTION ALGORITHM; SEQUENTIAL FORWARD SELECTION; VISUAL FIELDS;

ALGORITHMS; ELECTROENCEPHALOGRAPHY; ENTERPRISE RESOURCE PLANNING; EXPERIMENTS; FEATURE EXTRACTION; INTERFACES (COMPUTER); OPTICAL SENSORS;

BRAIN COMPUTER INTERFACE;

ADULT; ALGORITHM; ARTICLE; ATTENTION; BRAIN; COMPUTER INTERFACE; ELECTROENCEPHALOGRAPHY; EVOKED RESPONSE; FEMALE; HUMAN; IMAGINATION; MALE; METHODOLOGY; MOVEMENT (PHYSIOLOGY); PERCEPTIVE DISCRIMINATION; PHYSIOLOGY;

ADULT; ALGORITHMS; ATTENTION; BRAIN; DISCRIMINATION (PSYCHOLOGY); ELECTROENCEPHALOGRAPHY; EVOKED POTENTIALS; FEMALE; HUMANS; IMAGINATION; MALE; MOVEMENT; USER-COMPUTER INTERFACE;

EID: 77954458083     PISSN: 01400118     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11517-010-0578-1     Document Type: Article

References (34)

1. Babiloni C et al (1999) Human movement-related potentials vs desynchronization of EEG alpha rhythm: a high-resolution EEG study. NeuroImage 10:658-665
2. Bashashati A, Ward RK, Birch GE (2005) A new design of the asynchronous brain-computer interface using the knowledge of the path of features. In: Proc 2nd IEEE-EMBS conference on neural engineering, Arlington, VA, pp 101-104
3. Boostani R et al (2007) A comparison approach toward finding the best feature and classifier in cue-based BCI. Med Biol Eng Comput 45:403-412
4. Courville T, Thompson B (2001) Use of structure coefficients in published multiple regression articles: β is not enough. Educ Psychol Meas 61:229-248
5. Dias NS et al (2009) Feature Down-Selection in brain-computer Interfaces. In: Proc. of the 4th international IEEE EMBS conference on neural engineering. Antalya, Turkey, pp 323-326
6. Dillon WR, Mulani N, Frederick DG (1989) On the use of component scores in the presence of group structure. J Cons Res 16:106-112
7. Duda RO, Hart PE, Stork DG (2001) Pattern classification. Wiley, New York
8. Fabiani GE et al (2004) Conversion of EEG activity into cursor movement by a brain-computer interface (BCI). IEEE Trans Neural Syst Rehabil Eng 12:331-338
9. Fatourechi M et al (2006) Automatic user customization for improving the performance of a self-paced brain interface. Med Biol Eng Comput 44:1093-1104
10. Flury B (1997) A first course in multivariate statistics. Springer, New York
11. Grafton ST et al (1997) Premotor cortex activation during observation and naming of familiar tools. Neuroimage 6:231-236
12. Guger C et al (2001) Rapid prototyping of an EEG-based braincomputer interface (BCI). IEEE Trans Neural Syst Rehabil Eng 9:49-58
13. Guo F et al (2008) A brain-computer interface using motiononset visual evoked potential. J Neural Eng 5:477-485
14. Guyon I, Eliseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157-1182
15. Hillyard SA, Vogel EK, Luck SJ (1998) Sensory gain control (amplification) as a mechanism of selective attention: electrophysiological and neuroimaging evidence. Phil Trans R Soc Lond B 353:1257-1270
16. Jolliffe IT (2002) Principal component analysis. Springer, New York
17. Klemm M, Haueisen J, Ivanova G (2009) Independent component analysis: comparison of algorithms for the investigation of surface electrical brain activity. Med Biol Eng Comput 47:413-423
18. Kruglikov SY, Schiff SJ (2003) Interplay of EEG phase and auditory evoked neural activity. J Neurosci 2:10122-10127
19. Krusienski DJ et al (2008) Toward enhanced P300 speller performance. J Neurosci Meth 167:15-21
20. Lal TN et al (2004) Support vector channel selection in BCI. IEEE Trans Biomed Eng 51:1003-1010
21. Lee P-L et al (2008) Brain computer interface using flash onset and offset visual evoked potentials. Clin Neurophysiol 119:605-616
22. Liao X et al (2007) Combining spatial filters for the classification of singal-trial EEG in a finger movement task. IEEE Trans Biomed Eng 54:821-831
23. Luck SJ (2005) An introduction to the event-related potential technique. The MIT Press, Cambridge, MA
24. Millán J et al (2002) Relevant EEG features for the classification of spontaneous motor-related tasks. Biol Cybern 86:89-95
25. Müller T et al (2000) Selecting relevant electrode positions for classification tasks based on the electro-encephalogram. Med Biol Eng Comput 38:62-67
26. Pfurtscheller G, Neuper C (2001) Motor imagery and direct brain-computer communication. Proc IEEE 89:1123-1134
27. Schiff SJ (2005) Dangerous phase. Neuroinformatics 3:315-318
28. Schiff SJ et al (2005) Neuronal spatiotemporal pattern discrimination: the dynamical evolution of seizures. Neuroimage 28:1043-1055
29. Sun S, Zhang C (2006) Adaptive feature extraction for EEG signal classification. Med Biol Eng Comput 44:931-935
30. Wang Y, Makeig S (2009) Predicting intended movement direction using EEG from human posterior parietal cortex. In: Schmorrow DD et al (eds) Augmented cognition, HCII 2009. LNAI 5638, pp 437-446
31. Wascher E, Wauschkuhn B (1996) The interaction of stimulus-and response-related processesmeasured by event-related lateralizations of the EEG. Electroencephalogr Clin Neurophysiol 99:149-162
32. Wolpaw JR et al (2002) Brain-computer interfaces for communication and control. Clin Neurophysiol 113:767-791
33. Wolpaw JR, McFarland DJ (2004) Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans. Proc Natl Acad Sci USA 101:17849-17854
34. Yu L, Liu H (2004) Efficient feature selection via analysis of relevance and redundancy. J Mach Learn 5:1205-1224